System and method for body mass index relation to patient differing psychological stress effect on blood glucose dynamics in patients with insulin dependent diabetes

ABSTRACT

An insulin device configured to control insulin dispensing based on insulin sensitivity. The insulin device includes a processor configured to receive insulin dosing schedule information, psychological stress level data, and body mass index (BMI) data; a sensor configured to generate a blood glucose level measurement. The sensor is calibrated as a function of the psychological stress level data and the BMI data and the processor is configured to monitor and detect changes of the blood glucose level measurement that are determined to have occurred as a function of changes of the psychological stress level data, and identify a time when the BMI data counteracts a detected change in the blood glucose level measurement. The insulin device also includes an insulin dispensing valve controlled by the processor to change the insulin dosing schedule information in accordance with the counteracting BMI data.

RELATED APPLICATION

This application claims the benefit under 35 U.S.C. § 119 of U.S.Provisional Patent No. 62/459,096 filed on Feb. 15, 2017, the entirecontents of which is hereby incorporated by reference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This disclosure was made with government support under Grant No.DK106826 awarded by the National Institutes of Health. The U.S.government has certain rights in the disclosure.

FIELD

An aspect of an embodiment of the present disclosure provides a device,method, and computer readable medium to control dispensing of insulinbased on insulin sensitivity.

BACKGROUND INFORMATION

In patients with insulin dependent diabetes (T1DM), better management ofthe disease requires accounting for as many factors as possible that canaffect blood glucose (BG) during a day. Psychological stress is not apart of current insulin regimen design while it is known to have effectson endocrine system by changes in secretion of glucocorticoids,catecholamine, growth hormone and prolactin. (see document “[1]”Ranabir).

The impact of stress on blood glucose level has been investigatedpreviously and results were various. In a review published in 1985,Carter et al. put the results from three of their previous studiestogether to evaluate the reaction of blood glucose levels to increasedstress in patients with type I diabetes. The results showed that it wasnot possible to infer a stress induced hyperglycemia in these patients.Therefore, they concluded that the assumption of hyperglycemiaoccurrence in the presence of stress needs to be avoided in T1DMtreatment arrangement (see document “[2] Patek). In 1990, Halford et al.reported a significant patient-specific stress effect for half of the 15patients in the study who showed an increase in blood glucose levels.Authors inferred that stress was influential on blood glucose levels atleast in some patients with diabetes (see document “[3]” “Clinicalguidelines on the identification, evaluation, and treatment ofoverweight and obesity in adults: executive summary. Expert Panel on theIdentification, Evaluation, and Treatment of Overweight in Adults.,”).In another study published in 1990, a significant blood glucose responseto psychological stress was found and the type and magnitude of theresponse were observed to be affected by idiosyncratic factors (seedocument “[4]” Carter et al.). Later in 2000, Kramer et al. published astudy where the results supported the existence of a metabolicdisturbance by stress with an idiosyncratic variability in T1DM patients(see document “[5]” Halford et al). Finally, in Gonder-Frederick etal.'s recent study using the same data with this study, it was reportedthat psychological stress affects BG and the direction and magnitudewere patient specific (see document “[6]” Gonder-Frederick et al).

The techniques described here consider insulin sensitivity to be atime-varying physiological parameter and utilizes continuous glucosemonitoring and insulin delivery data to quantify insulin sensitivity ofthe patient over time. Further, insulin sensitivity can be used fortracking the state of the patient's condition and adjust the treatmentplans so that the appropriate treatment regime is applied to a patient.

BACKGROUND REFERENCES

The following patents, applications and publications as listed below andthroughout this document are hereby incorporated by reference in theirentirety herein (and which are not admitted to be prior art with respectto the present disclosure by inclusion in this section):

-   [1] S. Ranabir and K. Reetu, “Stress and hormones,” Indian J.    Endocrinol. Metab., vol. 15, no. 1, pp. 18-22, 2011.-   [2] S. Patek, D. Lv, E. A. Ortiz, C. Hughes-Karvetski, S.    Kulkarni, Q. Zhang, and M. D. Breton, “Empirical Representation of    Blood Glucose Variability in a Compartmental Model,” in Prediction    Methods for Blood Glucose Concentration, Springer International    Publishing, 2015, pp. 133-156.-   [3] “Clinical guidelines on the identification, evaluation, and    treatment of overweight and obesity in adults: executive summary.    Expert Panel on the Identification, Evaluation, and Treatment of    Overweight in Adults.,” Am. J. Clin. Nutr., vol. 68, no. 4, pp.    899-917, October 1998.-   [4] W. R. Carter, L. A. Gonder-Frederick, D. J. Cox, W. L. Clarke,    and D. R. Scott, “Effect of Stress on Blood Glucose in IDDM,”    Diabetes Care, vol. 8, no. 4, pp. 411-412, July 1985.-   [5] W. K. Halford, S. Cuddihy, and R. H. Mortimer, “Psychological    stress and blood glucose regulation in type I diabetic patients,”    Health Psychol. Off. J. Div. Health Psychol. Am. Psychol. Assoc.,    vol. 9, no. 5, pp. 516-528, 1990.-   [6] L. A. Gonder-Frederick, W. R. Carter, D. J. Cox, and W. L.    Clarke, “Environmental stress and blood glucose change in    insulin-dependent diabetes mellitus,” Health Psychol. Off. J. Div.    Health Psychol. Am. Psychol. Assoc., vol. 9, no. 5, pp. 503-515,    1990.-   [7] J. R. Kramer, J. Ledolter, G. N. Manos, and M. L. Bayless,    “Stress and metabolic control in diabetes mellitus: methodological    issues and an illustrative analysis,” Ann. Behav. Med. Publ. Soc.    Behav. Med., vol. 22, no. 1, pp. 17-28, 2000.-   [8] L. A. Gonder-Frederick, J. H. Grabman, B. Kovatchev, S.    Patek, A. Basu, J. E. Pinsker, Y. C. Kudva, C. A. Wakeman, E.    Dassau, C. Cobelli, H. C. Zisser, and F. C. Doyle III, “Is    Psychological Stress a Factor for Incorporation Into Future    Closed-Loop Systems?,” J. Diabetes Sci. Technol., 2016.-   [9] J. A. O. C, R. Gomez-Perez, G. Arata-Bellabarba, and V.    Villaroel, “Relationship Between Bmi, Total Testosterone, Sex    Hormone-Binding-Globulin, Leptin, Insulin and Insulin Resistance in    Obese Men,” Arch. Androl., vol. 52, no. 5, pp. 355-361, January    2006.-   [10] A. Lukanova, E. Lundin, A. Zeleniuch-Jacquotte, P. Muti, A.    Mure, S. Rinaldi, L. Dossus, A. Micheli, A. Arslan, P. Lenner, R. E.    Shore, V. Krogh, K. L. Koenig, E. Riboli, F. Berrino, G.    Hallmans, P. Stattin, P. Toniolo, and R. Kaaks, “Body mass index,    circulating levels of sex-steroid hormones, IGF-I and IGF-binding    protein-3: a cross-sectional study in healthy women,” Eur. J.    Endocrinol., vol. 150, no. 2, pp. 161-171, February 2004.-   [11] I. Kyrou and C. Tsigos, “Stress hormones: physiological stress    and regulation of metabolism,” Curr. Opin. Pharmacol., vol. 9, no.    6, pp. 787-793, December 2009.-   [12] A. Moan, A. Hℏieggen, G. Nordby, I. Os, I. Eide, and S. E.    Kjeldsen, “Mental stress increases glucose uptake during    hyperinsulinemia: associations with sympathetic and cardiovascular    responsiveness,” Metabolism., vol. 44, no. 10, pp. 1303-1307,    October 1995.-   [13] T. Touma, S. Takishita, Y. Kimura, H. Muratani, and K.    Fukiyama, “Mild mental stress increases insulin sensitivity in    healthy young men,” Clin. Exp. Hypertens. N.Y. N 1993, vol. 18, no.    8, pp. 1105-1114, November 1996.-   [14] G. Seematter, E. Guenat, P. Schneiter, C. Cayeux, E. Jéquier,    and L. Tappy, “Effects of mental stress on insulin-mediated glucose    metabolism and energy expenditure in lean and obese women,” Am. J.    Physiol. Endocrinol. Metab., vol. 279, no. 4, pp. E799-805, October    2000.-   [15] P. G. Kopelman, A. Grossman, P. Lavender, G. M. Besser, L. H.    Rees, and D. Coy, “The cortisol response to corticotrophin-releasing    factor is blunted in obesity,” Clin. Endocrinol. (Oxf.), vol. 28,    no. 1, pp. 15-18, January 1988.-   [16] M. Vranic, “Banting Lecture: Glucose Turnover: A Key to    Understanding the Pathogenesis of Diabetes (Indirect Effects of    Insulin),” Diabetes, vol. 41, no. 9, pp. 1188-1206, September 1992.    The following patents, applications and publications as listed below    and throughout this document are hereby incorporated by reference in    their entirety herein. It should be appreciated that various aspects    of embodiments of the present method, system, devices, article of    manufacture, computer readable medium, and compositions may be    implemented with the following methods, systems, devices, article of    manufacture, computer readable medium, and compositions disclosed in    the following U.S. Patent Applications, U.S. Patents, and PCT    International Patent Applications and are hereby incorporated by    reference herein and co-owned with the assignee (and which are not    admitted to be prior art with respect to the present disclosure by    inclusion in this section):-   A. International Patent Application No. PCT/US2017/015616 entitled    “METHOD, SYSTEM, AND COMPUTER READABLE MEDIUM FOR VIREALIZATION OF A    CONTINUOUS GLUCOSE MONITORING TRACE”, filed Jan. 30, 2017.-   B. International Patent Application No. PCT/US2016/058234 entitled    “System, Method and Computer Readable Medium for Dynamical Tracking    of the Risk for Hypoglycemia in Type 1 and Type 2 Diabetes”, filed    Oct. 21, 2016.-   C. International Patent Application No. PCT/US2016/054200 entitled    “GAIT PATHOLOGY DETECTION AND MONITORING SYSTEM, AND METHOD”, filed    Sep. 28, 2016.-   D. International Patent Application No. PCT/US2016/050109 entitled    “SYSTEM, METHOD, AND COMPUTER READABLE MEDIUM FOR DYNAMIC INSULIN    SENSITIVITY IN DIABETIC PUMP USERS”, filed Sep. 2, 2016; U.S. patent    application Ser. No. 15/255,828 entitled “SYSTEM, METHOD, AND    COMPUTER READABLE MEDIUM FOR DYNAMIC INSULIN SENSITIVITY IN DIABETIC    PUMP USERS”, filed Sep. 2, 2016.-   E. U.S. patent application Ser. No. 15/252,365 entitled “Method,    System and Computer Readable Medium for Predictive Hypoglycemia    Detection for Mild to Moderate Exercise”, filed Aug. 31, 2016.-   F. U.S. patent application Ser. No. 15/109,682 entitled “Central    Data Exchange Node For System Monitoring and Control of Blood    Glucose Levels in Diabetic Patients”, filed Jul. 5, 2016;    Publication No. US-2016-0331310-AI, Nov. 17, 2016; International    Patent Application No. PCT/US2015/010167 entitled “Central Data    Exchange Node For System Monitoring and Control of Blood Glucose    Levels in Diabetic Patients”, filed Jan. 5, 2015; Publication No.    WO2015103543, Jul. 9, 2015.-   G. International Patent Application No. PCT/US2016/036729 entitled    “CGM Based Fault Detection and Mitigation of Insulin    Delivery/Monitoring Systems Via Metabolic State Tracking”, filed    Jun. 9, 2016; Publication No. WO2016201120, Dec. 15, 2016.-   H. International Patent Application No. PCT/US2016/036481 entitled    “Hemoglobin A1c and Self-Monitored Average Glucose: Validation of    the Dynamical Tracking eA1c Algorithm in Type 1 Diabetes”, filed    Jun. 8, 2016; Publication No. WO2016200970, Dec. 15, 2016.-   I. International Patent Application No. PCT/US2016/018027 entitled    “Method, System and Computer Readable Medium for Assessing    Actionable Glycemic Risk”, filed Feb. 16, 2016; Publication No.    WO2016133879, Aug. 25, 2016.-   J. U.S. patent application Ser. No. 14/902,731 entitled “SIMULATION    OF ENDOGENOUS AND EXOGENOUS GLUCOSE/INSULIN/GLUCAGON INTERPLAY IN    TYPE 1 DIABETIC PATIENTS”, filed Jan. 4, 2016; Publication No.    US-2016-0171183-A1, Jun. 16, 2016; International Patent Application    No. PCT/US2014/045393 entitled “SIMULATION OF ENDOGENOUS AND    EXOGENOUS GLUCOSE/INSULIN/GLUCAGON INTERPLAY IN TYPE 1 DIABETIC    PATIENTS”, filed Jul. 3, 2014; Publication No. WO2015003124, Jan. 8,    2015.-   K. U.S. patent application Ser. No. 14/769,638 entitled “METHOD AND    SYSTEM FOR MODEL-BASED TRACKING OF CHANGES IN AVERAGE GLYCEMIA IN    DIABETES”, filed Aug. 21, 2015; Publication No. US-2016-0004813-A1,    Jan. 7, 2016; International Patent Application No. PCT/US2014/017754    entitled “METHOD AND SYSTEM FOR MODEL-BASED TRACKING OF CHANGES IN    AVERAGE GLYCEMIA IN DIABETES”, filed Feb. 21, 2014; Publication No.    WO 2014/130841, Aug. 28, 2014.-   L. International Patent Application No. PCT/US2015/045340 entitled    “IMPROVED ACCURACY CONTINUOUS GLUCOSE MONITORING METHOD, SYSTEM, AND    DEVICE”, filed Aug. 14, 2015; Publication No. WO2016025874, Feb. 18,    2016.-   M. U.S. patent application Ser. No. 14/799,329 entitled “Improving    the Accuracy of Continuous Glucose Sensors”, filed Jul. 14, 2015;    Publication No. US-2016-0007890-A1, Jan. 14, 2016; U.S. patent    application Ser. No. 12/065,257 entitled “Accuracy of Continuous    Glucose Sensors”, filed Feb. 28, 2008; Publication No. 2008/0314395,    Dec. 25, 2008; International Patent Application No.    PCT/US2006/033724 entitled “Method for Improvising Accuracy of    Continuous Glucose Sensors and a Continuous Glucose Sensor Using the    Same”, filed Aug. 29, 2006; Publication No. WO07027691, Mar. 8,    2007.-   N. U.S. patent application Ser. No. 14/419,375 entitled “COMPUTER    SIMULATION FOR TESTING AND MONITORING OF TREATMENT STRATEGIES FOR    STRESS HYPERGLYCEMIA”, filed Feb. 3, 2015; Publication No.    2015-0193589, Jul. 9, 2015; International Patent Application No.    PCT/US2013/053664 entitled “COMPUTER SIMULATION FOR TESTING AND    MONITORING OF TREATMENT STRATEGIES FOR STRESS HYPERGLYCEMIA”, filed    Aug. 5, 2013; Publication No. WO 2014/022864, Feb. 6, 2014.-   O. U.S. patent application Ser. No. 14/266,612 entitled “Method,    System and Computer Program Product for Real-Time Detection of    Sensitivity Decline in Analyte Sensors”, filed Apr. 30, 2014;    Publication No. 2014/0244216, Aug. 28, 2014; U.S. patent application    Ser. No. 13/418,305 entitled “Method, System and Computer Program    Product for Real-Time Detection of Sensitivity Decline in Analyte    Sensors”, filed Mar. 12, 2012; U.S. Pat. No. 8,718,958, issued May    6, 2014; U.S. patent application Ser. No. 11/925,689 entitled    “Method, System and Computer Program Product for Real-Time Detection    of Sensitivity Decline in Analyte Sensors”, filed Oct. 26, 2007;    U.S. Pat. No. 8,135,548, issued Mar. 13, 2012; International Patent    Application No. PCT/US2007/082744 entitled “Method, System and    Computer Program Product for Real-Time Detection of Sensitivity    Decline in Analyte Sensors”, filed Oct. 26, 2007; Publication No.    WO/2008/052199, May 2, 2008.-   P. U.S. patent application Ser. No. 14/241,383 entitled “Method,    System and Computer Readable Medium for Adaptive Advisory Control of    Diabetes”, filed Feb. 26, 2014; Publication No. 2015-0190098, Jul.    9, 2015; International Patent Application No. PCT/US2012/052422    entitled “Method, System and Computer Readable Medium for Adaptive    Advisory Control of Diabetes”, filed Aug. 26, 2012; Publication No.    WO 2013/032965, Mar. 7, 2013.-   Q. U.S. patent application Ser. No. 14/128,922 entitled “Unified    Platform For Monitoring and Control of Blood Glucose Levels in    Diabetic Patients”, filed Dec. 23, 2013; Publication No.    2015/0018633, Jan. 15, 2015; International Patent Application No.    PCT/US2012/043910 entitled “Unified Platform For Monitoring and    Control of Blood Glucose Levels in Diabetic Patients”, filed Jun.    23, 2012; Publication No. WO 2012/178134, Dec. 27, 2012.-   R. U.S. patent application Ser. No. 14/128,811 entitled “Methods and    Apparatus for Modular Power Management and Protection of Critical    Services in Ambulatory Medical Devices”, filed Dec. 23, 2013; U.S.    Pat. No. 9,430,022, issued Aug. 30, 2016; International Patent    Application No. PCT/US2012/043883 entitled “Methods and Apparatus    for Modular Power Management and Protection of Critical Services in    Ambulatory Medical Devices”, filed Jun. 22, 2012; Publication No. WO    2012/178113, Dec. 27, 2012.-   S. U.S. patent application Ser. No. 14/015,831 entitled “CGM-Based    Prevention of Hypoglycemia Via Hypoglycemia Risk Assessment and    Smooth Reduction Insulin Delivery”, filed Aug. 30, 2013; Publication    No. 20140046159, Feb. 13, 2014; U.S. patent application Ser. No.    13/203,469 entitled “CGM-Based Prevention of Hypoglycemia via    Hypoglycemia Risk Assessment and Smooth Reduction Insulin Delivery”,    filed Aug. 25, 2011; U.S. Pat. No. 8,562,587, issued Oct. 22, 2013;    International Patent Application No. PCT/US2010/025405 entitled    “CGM-Based Prevention of Hypoglycemia via Hypoglycemia Risk    Assessment and Smooth Reduction Insulin Delivery”, filed Feb. 25,    2010; Publication No. WO 2010/099313 A1, Sep. 2, 2010.-   T. International Patent Application No. PCT/US2013/042745 entitled    “INSULIN-PRAMLINTIDE COMPOSITIONS AND METHODS FOR MAKING AND USING    THEM”, filed May 24, 2013; Publication No. WO 2013/177565, Nov. 28,    2013; U.S. patent application Ser. No. 14/403,177 entitled    “INSULIN-PRAMLINTIDE COMPOSITIONS AND METHODS FOR MAKING AND USING    THEM”.-   U. U.S. patent application Ser. No. 13/637,359 entitled “METHOD,    SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR IMPROVING THE ACCURACY OF    GLUCOSE SENSORS USING INSULIN DELIVERY OBSERVATION IN DIABETES”,    filed Sep. 25, 2012; U.S. Pat. No. 9,398,869, issued Jul. 26, 2016;    International Patent Application No. PCT/US2011/029793 entitled    “METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR IMPROVING THE    ACCURACY OF GLUCOSE SENSORS USING INSULIN DELIVERY OBSERVATION IN    DIABETES”, filed Mar. 24, 2011; Publication No. WO 2011/119832, Sep.    29, 2011.-   V. U.S. patent application Ser. No. 13/634,040 entitled “Method and    System for the Safety, Analysis, and Supervision of Insulin Pump    Action and Other Modes of Insulin Delivery in Diabetes”, filed Sep.    11, 2012; Publication No. 2013/0116649, May 9, 2013; International    Patent Application No. PCT/US2011/028163 entitled “Method and System    for the Safety, Analysis, and Supervision of Insulin Pump Action and    Other Modes of Insulin Delivery in Diabetes”, filed Mar. 11, 2011;    Publication No. WO 2011/112974, Sep. 15, 2011.-   W. U.S. patent application Ser. No. 13/394,091 entitled “Tracking    the Probability for Imminent Hypoglycemia in Diabetes from    Self-Monitoring Blood Glucose (SMBG) Data”, filed Mar. 2, 2012;    Publication No. 2012/0191361, Jul. 26, 2012; International Patent    Application No. PCT/US2010/047711 entitled “Tracking the Probability    for Imminent Hypoglycemia in Diabetes from Self-Monitoring Blood    Glucose (SMBG) Data”, filed Sep. 2, 2010; Publication No. WO    2011/028925, Mar. 10, 2011.-   X. U.S. patent application Ser. No. 13/393,647 entitled “System,    Method and Computer Program Product for Adjustment of Insulin    Delivery (AID) in Diabetes Using Nominal Open-Loop Profiles”, filed    Mar. 1, 2012; Publication No. 2012/0245556, Sep. 27, 2012;    International Patent Application No. PCT/US2010/047386 entitled    “System, Method and Computer Program Product for Adjustment of    Insulin Delivery (AID) in Diabetes Using Nominal Open-Loop    Profiles”, filed Aug. 31, 2010; Publication No. WO 2011/028731, Mar.    10, 2011.-   Y. U.S. patent application Ser. No. 13/380,839 entitled “System,    Method, and Computer Simulation Environment for In Silico Trials in    Pre-Diabetes and Type 2 Diabetes”, filed Dec. 25, 2011; Publication    No. 2012/0130698, May 24, 2012; International Patent Application No.    PCT/US2010/040097 entitled “System, Method, and Computer Simulation    Environment for In Silico Trials in Prediabetes and Type 2    Diabetes”, filed Jun. 25, 2010; Publication No. WO 2010/151834, Dec.    29, 2010.-   Z. U.S. patent application Ser. No. 13/322,943 entitled “System    Coordinator and Modular Architecture for Open-Loop and Closed-Loop    Control of Diabetes”, filed Nov. 29, 2011; Publication No.    2012/0078067, Mar. 29, 2012; International Patent Application No.    PCT/US2010/036629 entitled “System Coordinator and Modular    Architecture for Open-Loop and Closed-Loop Control of Diabetes”,    filed May 28, 2010; Publication No. WO 2010/138848, Dec. 2, 2010.-   AA. U.S. patent application Ser. No. 13/131,467 entitled “Method,    System, and Computer Program Product for Tracking of Blood Glucose    Variability in Diabetes”, filed May 26, 2011; U.S. Pat. No.    9,317,657, issued Apr. 19, 2016; International Patent Application    No. PCT/US2009/065725 entitled “Method, System, and Computer Program    Product for Tracking of Blood Glucose Variability in Diabetes”,    filed Nov. 24, 2009; Publication No. WO 2010/062898, Jun. 3, 2010.-   BB. U.S. patent application Ser. No. 12/975,580 entitled “Method,    System, and Computer Program Product for the Evaluation of Glycemic    Control in Diabetes from Self-Monitoring Data”, filed Dec. 22, 2010;    Publication No. 2012/0004512, Jan. 5, 2012; U.S. patent application    Ser. No. 11/305,946 entitled “Method, System, and Computer Program    Product for the Evaluation of Glycemic Control in Diabetes from    Self-Monitoring Data”, filed Dec. 19, 2005; U.S. Pat. No. 7,874,985,    issued Jan. 25, 2011; U.S. patent application Ser. No. 10/240,228    entitled “Method, System, and Computer Program Product for the    Evaluation of Glycemic Control in Diabetes from Self-Monitoring    Data”, filed Sep. 26, 2002; U.S. Pat. No. 7,025,425, issued Apr. 11,    2006; International Patent Application No. PCT/US2001/009884    entitled “Method, System, and Computer Program Product for the    Evaluation of Glycemic Control in Diabetes”, filed Mar. 29, 2001;    Publication No. WO 01/72208, Oct. 4, 2001.-   CC. U.S. patent application Ser. No. 12/665,420 entitled “LQG    Artificial Pancreas Control System and Related Method”, filed Dec.    18, 2009; Publication No. 2010/0249561, Sep. 30, 2010; International    Patent Application No. PCT/US2008/067723 entitled “LQG Artificial    Pancreas Control System and Related Method”, filed Jun. 20, 2008;    Publication No. WO 2008/157780, Dec. 24, 2008.-   DD. U.S. patent application Ser. No. 12/665,149 entitled “Method,    System and Computer Program Product for Evaluation of Insulin    Sensitivity, Insulin/Carbohydrate Ratio, and Insulin Correction    Factors in Diabetes from Self-Monitoring Data”, filed Dec. 17, 2009;    Publication No. 2010/0198520, Aug. 5, 2010; International Patent    Application No. PCT/US2008/069416 entitled “Method, System and    Computer Program Product for Evaluation of Insulin Sensitivity,    Insulin/Carbohydrate Ratio, and Insulin Correction Factors in    Diabetes from Self-Monitoring Data”, filed Jul. 8, 2008; Publication    No. WO 2009/009528, Jan. 15, 2009.-   EE. U.S. patent application Ser. No. 12/664,444 entitled “Method,    System and Computer Simulation Environment for Testing of Monitoring    and Control Strategies in Diabetes”, filed Dec. 14, 2009;    Publication No. 2010/0179768, Jul. 15, 2010; International Patent    Application No. PCT/US2008/067725 entitled “Method, System and    Computer Simulation Environment for Testing of Monitoring and    Control Strategies in Diabetes”, filed Jun. 20, 2008; Publication    No. WO 2008/157781, Dec. 24, 2008-   FF. U.S. patent application Ser. No. 12/516,044 entitled “Method,    System, and Computer Program Product for the Detection of Physical    Activity by Changes in Heart Rate, Assessment of Fast Changing    Metabolic States, and Applications of Closed and Open Control Loop    in Diabetes”, filed May 22, 2009; U.S. Pat. No. 8,585,593, issued    Nov. 19, 2013; International Patent Application No.    PCT/US2007/085588 entitled “Method, System, and Computer Program    Product for the Detection of Physical Activity by Changes in Heart    Rate, Assessment of Fast Changing Metabolic States, and Applications    of Closed and Open Control Loop in Diabetes”, filed Nov. 27, 2007;    Publication No. WO2008/067284, Jun. 5, 2008.-   GG. U.S. patent application Ser. No. 12/159,891 entitled “Method,    System and Computer Program Product for Evaluation of Blood Glucose    Variability in Diabetes from Self-Monitoring Data”, filed Jul. 2,    2008; Publication No. 2009/0171589, Jul. 2, 2009; International    Patent Application No. PCT/US2007/000370 entitled “Method, System    and Computer Program Product for Evaluation of Blood Glucose    Variability in Diabetes from Self-Monitoring Data”, filed Jan. 5,    2007; Publication No. WO07081853, Jul. 19, 2007.-   HH. U.S. patent application Ser. No. 11/943,226 entitled “Systems,    Methods and Computer Program Codes for Recognition of Patterns of    Hyperglycemia and Hypoglycemia, Increased Glucose Variability, and    Ineffective Self-Monitoring in Diabetes”, filed Nov. 20, 2007;    Publication No. 2008/0154513, Jun. 26, 2008.-   II. U.S. patent application Ser. No. 11/578,831 entitled “Method,    System and Computer Program Product for Evaluating the Accuracy of    Blood Glucose Monitoring Sensors/Devices”, filed Oct. 18, 2006; U.S.    Pat. No. 7,815,569, issued Oct. 19, 2010; International Patent    Application No. US2005/013792 entitled “Method, System and Computer    Program Product for Evaluating the Accuracy of Blood Glucose    Monitoring Sensors/Devices”, filed Apr. 21, 2005; Publication No.    WO05106017, Nov. 10, 2005.-   JJ. U.S. patent application Ser. No. 10/524,094 entitled “Method,    System, And Computer Program Product For The Processing Of    Self-Monitoring Blood Glucose (SMBG) Data To Enhance Diabetic    Self-Management”, filed Feb. 9, 2005; Publication No. 2005214892,    Sep. 29, 2005; International Patent Application No.    PCT/US2003/025053 entitled “Managing and Processing Self-Monitoring    Blood Glucose”, filed Aug. 8, 2003; Publication No. WO 2004/015539,    Feb. 19, 2004.-   KK. U.S. patent application Ser. No. 10/069,674 entitled “Method and    Apparatus for Predicting the Risk of Hypoglycemia”, filed Feb. 22,    2002; U.S. Pat. No. 6,923,763, issued Aug. 2, 2005; International    Patent Application No. US00/22886 entitled “METHOD AND APPARATUS FOR    PREDICTING THE RISK OF HYPOGLYCEMIA”, filed Aug. 21, 2000;    Publication No. WO01/13786, Mar. 1, 2001.

SUMMARY

An insulin device configured to control insulin dispensing based oninsulin sensitivity is disclosed, the insulin device comprising aprocessor configured to receive insulin dosing schedule information,psychological stress level data, and body mass index (BMI) data; asensor configured to generate a blood glucose level measurement, whereinthe sensor is calibrated as a function of the psychological stress leveldata and the BMI data, and wherein the processor is configured tomonitor and detect changes of the blood glucose level measurement thatare determined to have occurred as a function of changes of thepsychological stress level data, and identify a time when the BMI datacounteracts a detected change in the blood glucose level measurement;and an insulin dispensing valve controlled by the processor to changethe insulin dosing schedule information in accordance with thecounteracting BMI data.

A computer-implemented method to control insulin dispensing based oninsulin sensitivity is also disclosed, the method comprising receivinginsulin dosing schedule information, psychological stress level data,and body mass index (BMI) data generating a blood glucose levelmeasurement as a function of the psychological stress level data and theBMI data

-   -   monitoring and detecting changes of the blood glucose level        measurement that are determined to have occurred as a function        of changes of the psychological stress level data identifying a        time when the BMI data counteracts a detected change in the        blood glucose level measurement    -   updating the insulin dosing schedule information in accordance        with the counteracting BMI data and controlling an insulin        dispensing device to provide insulin dosing based on the updated        insulin dosing schedule information.

A non-transitory computer readable recording medium encoded with acomputer program is disclosed comprising program instructions causing aninsulin device to control insulin dispensing based on insulinsensitivity, the program causing the insulin device to receive insulindosing schedule information, psychological stress level data, and bodymass index (BMI) data; generate a blood glucose level measurement as afunction of the psychological stress level data and the BMI data;monitor and detect changes of the blood glucose level measurement thatare determined to have occurred as a function of changes of thepsychological stress level data; identify a time when the BMI datacounteracts a detected change in the blood glucose level measurement;update the insulin dosing schedule information in accordance with thecounteracting BMI data; and control an insulin dispensing device toprovide insulin dosing based on the updated insulin dosing scheduleinformation.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects and advantages of the present disclosure will becomeapparent to those skilled in the art upon reading the following detaileddescription of exemplary embodiments, in conjunction with theaccompanying drawings, in which like reference numerals have been usedto designate like elements, and in which:

FIG. 1 illustrates the stress effect on Net Effect Mean by BMIcategories.

FIG. 2 is a high level functional block diagram of an embodiment of thepresent disclosure, or an aspect of an embodiment of the presentdisclosure.

FIG. 3A illustrates a computing device in upon which an embodiment ofthe disclosure can be implemented.

FIG. 3B illustrates a network system in which an embodiment of thedisclosure can be implemented.

FIG. 4 is a block diagram that illustrates a system including a computersystem and the associated Internet connection upon which an embodimentmay be implemented.

FIG. 5 illustrates a system in which one or more embodiments of thedisclosure can be implemented using a network, or portions of a networkor computers.

FIG. 6 is a block diagram illustrating an example of a machine uponwhich one or more aspects of embodiments of the present disclosure canbe implemented.

DETAILED DESCRIPTION

Insulin sensitivity, among other factors, depends on the psychologicalstress of the patient. Additionally, as described herein, the applicanthas determined that a change in the stress level for patients withdifferent BMIs does not indicate a common pattern of change in the bloodglucose levels. That is, a change in the blood glucose level with achange in stress of different diabetic patients is sporadic. Thissporadic phenomenon leads to a less than optimal dosing in patients withdifferent stress levels or patients with frequently changing stresslevels.

The present disclosure is directed to providing an improved insulindevice method, and computer readable medium configured to controlinsulin dispensing based on insulin sensitivity. The device includes acomputer configured to administer insulin to a patient in a moreeffective manner by considering a previously uncorrelated relationshipbetween the body mass index (BMI) and the stress level of the patient.For example, insulin can be dispensed more accurately and precisely toachieve enhanced results with decreased volume. The device uses theforegoing relationship to determine a magnitude and direction of changein the blood glucose levels according to changes in the stress levels.Accordingly, the device changes the insulin dosage in accordance withthe change in the blood glucose levels, thereby providing an efficientway of supplying insulin in a more effective manner. For example, tomaintain the appropriate blood glucose level, patients with a highstress level and a higher BMI may require less insulin dosage thanpatients with the same stress level but a lower BMI. In this example,other factors that affect insulin dosage such as meal, physical activityare considered the same for both patients.

The insulin device includes a processor 102 as illustrated in, forexample, FIG. 2 that communicates with the glucose monitor or device101, and optionally the insulin device 100. The processor or controller102 is configured to perform the specified calculations. Optionally, theinsulin device 100 communicates with the subject 103 to deliver insulinto the subject 103. The processor or controller 102 is configured toperform the specified calculations. The glucose monitor 101 and theinsulin device 100 may be implemented as a separate device or as asingle device. The processor 102 can be implemented locally in theglucose monitor 101, the insulin device 100, or a standalone device (orin any combination of two or more of the glucose monitor, insulindevice, or a stand along device). The processor 102 or a portion of thesystem can be located remotely such that the device is operated as atelemedicine device. The term “processor” is meant to include anyintegrated circuit or other electronic device (or collection of devices)capable of performing an operation on at least one instructionincluding, without limitation, Reduced Instruction Set Core (RISC)processors, CISC microprocessors, Microcontroller Units (MCUs),CISC-based Central Processing Units (CPUs), and Digital SignalProcessors (DSPs). The hardware of such devices may be integrated onto asingle substrate (e.g., silicon “die”), or distributed among two or moresubstrates. Furthermore, various functional aspects of the processor maybe implemented solely as software or firmware associated with theprocessor.

The processor 102 is configured to receive insulin dosing scheduleinformation. The insulin dosing schedule information can be inputtedusing an input device 132 or can be obtained from an insulin pump 10. Inan embodiment the insulin pump may be implemented by the subject (orpatient) locally at home or other desired location. However, in analternative embodiment it may be implemented in a clinic setting orassistance setting. For instance, referring to FIG. 5 , a clinic setup158 provides a place for doctors (e.g. 164) or clinician/assistant todiagnose patients (e.g. 159) with diseases related with glucose andrelated diseases and conditions.

The processor 102 is configured to receive psychological stress leveldata. The psychological stress level is measured on a scale. In anexemplary embodiment, the scale can be 5-point Likert-type scale where 0represents no stress and 4 represents extreme stress. In a preferredembodiment, the value of the psychological stress level data is 1 on the5-point Likert-type scale. In another preferred embodiment, the value ofthe psychological stress level data is 2 on the 5-point Likert-typescale.

The processor 102 is configured to receive body mass index (BMI) data.The BMI data categorizes patients according to clinical guidelines ofBMI identification (see document “[8]” (Kramer) into normal BMI,overweight BMI, or obese BMI.

A relationship between psychological stress level and BMI describedherein, was identified by a research involving a total of thirty-eightT1DM patients of age range 21-65 years with HbA1c <10% and with use ofinsulin pump for at least 6 months. Pregnancy, diabetic ketoacidosis orsevere hypoglycemia in the 12 months prior to enrollment, history of aseizure disorder, medical conditions and drug use that might interferewith the completion of study was exclusion criteria. Informed consentwas obtained from all patients.

A sensor is configured to generate a blood glucose level measurement,for example, of the patients 159. In an exemplary embodiment, the sensorcan be a glucose monitor 101 that communicates with the subject 103 tomonitor glucose levels. FIG. 5 illustrates an exemplary systemdescribing an embodiment where the glucose monitor or glucose meter(and/or insulin pump) may be implemented by the subject (or patient)locally at home or other desired location. However, in an alternativeembodiment it may be implemented in a clinic setting or assistancesetting. For instance, referring to FIG. 5 , a clinic setup 158 providesa place for doctors (e.g. 164) or clinician/assistant to diagnosepatients (e.g. 159) with diseases related with glucose and relateddiseases and conditions. A glucose monitoring device 10 can be used tomonitor and/or test the glucose levels of the patient—as a standalonedevice. It should be appreciated that while only glucose monitor device10 is shown in the figure, the system of the exemplary embodiment andany component thereof may be used in the manner depicted by FIG. 5 .

The system or component may be affixed to the patient or incommunication with the patient as desired or required. For example thesystem or combination of components thereof—including a glucose monitordevice 10 (or other related devices or systems such as a controller,and/or an insulin pump, or any other desired or required devices orcomponents)—may be in contact, communication or affixed to the patientthrough tape or tubing (or other medical instruments or components) ormay be in communication through wired or wireless connections. Suchmonitor and/or test can be short term (e.g. clinical visit) or long term(e.g. clinical stay or family). The glucose monitoring device outputscan be used by the doctor (clinician or assistant) for appropriateactions, such as insulin injection or food feeding for the patient, orother appropriate actions or modeling.

Alternatively, the glucose monitoring device output can be delivered tocomputer terminal 168 for instant or future analyses. The delivery canbe through cable or wireless or any other suitable medium. The glucosemonitoring device output from the patient can also be delivered to aportable device, such as PDA 166. The glucose monitoring device outputswith improved accuracy can be delivered to a glucose monitoring center172 for processing and/or analyzing. Such delivery can be accomplishedin many ways, such as network connection 170, which can be wired orwireless.

In addition to the glucose monitoring device outputs, errors, parametersfor accuracy improvements, and any accuracy related information can bedelivered, such as to computer 168, and/or glucose monitoring center 172for performing error analyses. This can provide a centralized accuracymonitoring, modeling and/or accuracy enhancement for glucose centers,due to the importance of the glucose sensors.

Embodiments of the disclosure can also be implemented in a standalonecomputing device associated with the target glucose monitoring device.An exemplary computing device (or portions thereof) 144 in whichexamples of the disclosure can be implemented is schematicallyillustrated in FIG. 3A. In an exemplary configuration, computing device144 can include at least one processing unit 150 and memory 146.Depending on the exact configuration and type of computing device,memory 146 can be volatile (such as RAM), non-volatile (such as ROM,flash memory, etc.) or some combination of the two.

In an exemplary embodiment, the computing device 144 may also have otherfeatures and/or functionality. For example, the device could alsoinclude additional removable and/or non-removable storage including, butnot limited to, magnetic or optical disks or tape, as well as writableelectrical storage media. Such additional storage is the figure byremovable storage 152 and non-removable storage 148. Computer storagemedia includes volatile and nonvolatile, removable and non-removablemedia implemented in any method or technology for storage of informationsuch as computer readable instructions, data structures, program modulesor other data. The memory, the removable storage and the non-removablestorage are all examples of computer storage media. Computer storagemedia includes, but is not limited to, RAM, ROM, EEPROM, flash memory orother memory technology CDROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can accessed by thedevice. Any such computer storage media may be part of, or used inconjunction with, the device.

The computing device 144 may also contain one or more communicationsconnections 154 that allow the device to communicate with other devices(e.g. other computing devices). The communications connections carryinformation in a communication media. Communication media typicallyembodies computer readable instructions, data structures, programmodules or other data in a modulated data signal such as a carrier waveor other transport mechanism and includes any information deliverymedia. The term “modulated data signal” means a signal that has one ormore of its characteristics set or changed in such a manner as toencode, execute, or process information in the signal. By way ofexample, and not limitation, communication medium includes wired mediasuch as a wired network or direct-wired connection, and wireless mediasuch as radio, RF, infrared and other wireless media. As discussedabove, the term computer readable media as used herein includes bothstorage media and communication media.

The processor 102 is also configured to correlate a detected increase ina value of the psychological stress level data with an increase in avalue of the blood glucose level measurement and a detected decrease ina value of the psychological stress level data with a decrease in avalue of the blood glucose level measurement. In the research, for eachparticipant, continuous blood glucose monitor (CGM) readings werecollected for 7 consecutive days and net effect was computed for alldays except the first and last days (mean=5±1 day/patient).Additionally, days with CGM gaps with more than 3 consecutive hours ormore than sixty missing CGM values were excluded. Remaining datasetconsisted of 188 days from thirty-seven patients with age range 25-62years (mean=46.8±10.8), HbA1c range 5.7-9.9% (mean=7.4±0.98) and BMIrange 21.5-39.4 kg/m² (mean=28.2±4.9).

The sensor is calibrated as a function of the psychological stress leveldata and the BMI data to recognize the stress effect on glycaemia. Tobetter understand this relationship, the present disclosure provides avariable called “net effect”. A change in the net effect impliespresence of a factor that affects blood glucose and that this factor isdifferent from meal intake and injected insulin. However, a change inblood glucose itself does not provide enough information to inferwhether it is due to a change in eating/bolus behavior or another factorlike psychological stress. Therefore, the research further investigatedchanges in net effect to infer the glycemic effect of daily stress inT1DM patients and found that net effect can take a positive or negativevalue and it is expected to be around zero for well recorded meal andbolus. The higher the absolute net effect is, the higher the deviationof blood glucose is from its estimated value by the inputs (i.e. mealintake and insulin bolus). More specifically, a negative net effectrepresents a lower blood glucose occurrence than the estimated value. Inthe same manner, a positive net effect value represents a higher bloodglucose occurrence than the estimate.

Linear mixed effects models were designed to explore (1) net effectmean, (2) total carbohydrate intake and (3) total bolus-carbohydrateintake ratio with patient effect being modeled as random factor wherestress and BMI were fixed factors. The reasoning behind net effectconcept allowed this model to show whether there is a difference betweenthe real and estimated blood glucose as a result of stress.

To identify this difference, the processor 102 is configured to monitorand detect changes of the blood glucose level measurement that aredetermined to have occurred as a function of changes of thepsychological stress level data and identify a time when the BMI datacounteracts a detected change in the blood glucose level measurement.This allows investigating (1) whether glycaemia is affected by stressand (2) whether body mass index is an influential factor on the glycemiceffect of stress. Considering possible inaccuracy that might stem fromsubjectivity in self-evaluation of stress level, the research comparedno stress (stress level 0) versus some stress (stress levels 1-2-3-4combined) before exploring whether the effect differs by stress level.In doing so, factors with p-value less than 0.05 were consideredsignificant. The results identified that stress was not found to resultin a common blood glucose pattern towards increase or decrease (p=0.26for overall stress, p>0.2 for stress levels 1, 2, 3 and 4). Rather, itseffect was found to be dependent on body mass index of the patient(p=0.01). Additionally, it was identified that the Stress-BMIinteraction had a negative coefficient −7 (p=0.01) opposing the effectof stress that had a positive coefficient 183.16 (p=0.02) (see Table 1).

TABLE 1 Stress Effect (SE) in Relation with BMI Parameter Value StressEffect (SE) p-value Intercept −66.11 72.03 0.36 Stress 183.16 77.930.02* BMI 2.07  2.43 0.40 Stress × BMI −7.00  2.70 0.01*

Another LME model was designed to explore whether the BMI×Stress effectdiffers based on stress level. This time, stress was modeled asoriginally reported by the patients with values 0 to 4. Results showedsignificant difference from baseline stress level-0 for both BMI×Stresslevel-1 (p=0.01) and BMI×Stress level-2 (p=0.007) (see Table 2).

TABLE 2 Effect of Different Stress Levels on Net Effect Mean in Relationwith BMI Parameter Value Stress Effect (SE) p-value Fixed EffectsIntercept −61.44 71.66 0.39 Stress Level 1 204.03 86.80 0.02* StressLevel 2 253.31 97.23 0.01* Stress Level 3 −90.28 141.50 0.52 StressLevel 4 −302.79 255.05 0.24 BMI 1.92 2.42 0.43 Interaction Stress Level1 × BMI −7.86 3.05 0.01* Stress Level 2 × BMI −9.43 3.47 0.007* StressLevel 3 × BMI 1.97 4.76 0.68 Stress Level 4 × BMI 10.78 9.18 0.24

Both models suggest that the processor 102 correlates a counteractioneffect by BMI, on the increase of blood glucose with increasing stress,increases with an increase in the value of BMI. That is, the higher theBMI is, the higher the counter effect becomes. The processor 102 alsodetects that when the increase in the value of the BMI data crosses athreshold, the counteraction results in a decrease in a value of theblood glucose level measurement. That is, after a certain point, countereffect exceeds a threshold and results in a decrease in the bloodglucose.

Visualization of data for different BMI categories also supports thatthe effect of stress on net effect changes based on body mass index (seeFIG. 1 ). This is to say that stress has an effect on blood glucoseindependent from food intake and insulin injection related changes andthis effect is BMI dependent. It also shows that our dataset did nothave enough data for stress levels 3&4 at all BMI levels.

Analyses were conducted also only for the days where patients did notexercise (n=103 days) in order to eliminate possible confounding effectof exercise and the same pattern was sustained (p=0.0014 for overallstress, p=0.008 for stress level-1, p=0.004 for stress level-2).

The research also analyzed changes in daily carbohydrate (CHO) intakeand daily bolus taken per unit carbohydrate (Total daily bolus/Total CHOintake). 4 days were excluded from the analysis due to no reportedcarbohydrate intake and LME models were designed for the remained184-days data. There was no evidence for a change in CHO intake foroverall stress (stress levels 1, 2, 3 and 4 combined). However, analysessuggested an increase in daily carbohydrate intake (p=0.03) and adecrease in daily bolus per 1 gr of carbohydrate for stress level-2(p=0.02) as BMI increases (see Table 3).

TABLE 3 Daily CHO Intake and Insulin Bolus per Unit CHO by Stress LevelCarbohydrate Intake Insulin per 1 gr CHO Parameter Value SE p-valueValue SE p-value Fixed Effects Intercept 245.8 75.80 0.0015 −0.35 0.160.04 Stress Level 1 −42.98 83.82 0.61 0.20 0.13 0.11 Stress Level 2−193.33 92.93 0.04 0.02 0.14 0.03 Stress Level 3 19.53 132.65 0.88 0.290.19 0.90 Stress Level 4 −19.53 251.80 0.94 0.096 0.36 0.79 BMI −2.192.59 0.40 0.018 0.006 0.004 Interaction Stress Level 1 × BMI 2.31 2.960.44 −0.008 0.005 0.07 Stress Level 2 × BMI 7.40 3.33 0.03 −0.011 0.0050.02 Stress Level 3 × BMI −0.67 4.48 0.88 −0.0016 0.007 0.8 Stress Level4 × BMI 0.23 9.33 0.98 −0.0027 0.013 0.84

In an exemplary embodiment, by using the techniques disclosed hereinthat contemplate the time-varying nature of insulin sensitivity based onthe stress level data and the BMI data of a patient, an optimal level ofinsulin dosage can be administered. In an exemplary embodiment, theinsulin dosage can be administered using an insulin dispensing valvethat is controlled by the processor 102 to change an insulin dosingschedule in accordance with the counteracting BMI data. In an exemplaryembodiment, the insulin dispensing valve has a retractable needlecannula and can be attached to the insulin device. The processor 102 cancontrol the insulin delivered by the insulin dispensing valve to providean optimal amount to a patient with a particular stress level and BMI.This provides a more appropriate insulin dosage by employing aclosed-loop control system as disclosed herein.

In an exemplary embodiment, the processor 102 is configured to correlatethe counteraction by the BMI data to a higher uptake of glucose. Assuch, T1DM and BMI related alterations in endocrine responses and higherincrease in energy expenditure with stress in people with higher BMI areresponsible for the observed effect. The novelty of this disclosure liesin (1) the use of net effect to examine stress effect on blood glucoseby excluding effects of meal and insulin (2) the inclusion of body massindex as a factor to explain a part of the idiosyncratic pattern ofstress effect. As described herein, analyses show that body mass indexinteracts with the stress effect and has a role in the direction andmagnitude of its effect. BMI is one of the factors that led to thedifferent observations and conclusion of idiosyncratic responses tomental stress that were reported for patients with T1DM prior to thisstudy.

In addition to a stand-alone computing machine, embodiments of thedisclosure can also be implemented on a network system comprising aplurality of computing devices that are in communication with anetworking means, such as a network with an infrastructure or an ad hocnetwork. The network connection can be wired connections or wirelessconnections.

As a way of example, FIG. 3B illustrates a network system in whichembodiments of the disclosure can be implemented. In this example, thenetwork system includes computer 156 (e.g. a network server), networkconnection means 158 (e.g. wired and/or wireless connections), computerterminal 160, and PDA (e.g. a smart-phone) 162 (or other handheld orportable device, such as a cell phone, laptop computer, tablet computer,GPS receiver, mp3 player, handheld video player, pocket projector, etc.or handheld devices (or non-portable devices) with combinations of suchfeatures). In an embodiment, it should be appreciated that the modulelisted as 156 may be glucose monitor device. In an embodiment, it shouldbe appreciated that the module listed as 156 may be a glucose monitordevice (or glucose meter) and/or an insulin device.

Any of the components shown or discussed with FIG. 3B may be multiple innumber. The embodiments can be implemented in any of the devices of thesystem. For example, execution of the instructions or other desiredprocessing can be performed on the same computing device that is any of156, 160, and 162. Alternatively, an embodiment can be performed ondifferent computing devices of the network system. For example, certaindesired or required processing or execution can be performed on one ofthe computing devices of the network (e.g. server 156 and/or glucosemonitor device), whereas other processing and execution of theinstruction can be performed at another computing device (e.g. terminal160) of the network system, or vice versa. In fact, certain processingor execution can be performed at one computing device (e.g. server 156and/or glucose monitor device); and the other processing or execution ofthe instructions can be performed at different computing devices thatmay or may not be networked. For example, the certain processing can beperformed at terminal 160, while the other processing or instructionsare passed to device 162 where the instructions are executed. Thisscenario may be of particular value especially when the PDA 162 device,for example, accesses to the network through computer terminal 160 (oran access point in an ad hoc network). For another example, software tobe protected can be executed, encoded or processed with one or moreembodiments as disclosed. The processed, encoded or executed softwarecan then be distributed to customers. The distribution can be in a formof storage media (e.g. disk) or electronic copy.

FIG. 4 is a block diagram that illustrates a system 130 including acomputer system 140 and the associated Internet 11 connection upon whichan embodiment may be implemented. Such configuration can be used forcomputers (hosts) connected to the Internet 11 and executing a server ora client (or a combination) software. A source computer such as laptop,an ultimate destination computer and relay servers, for example, as wellas any computer or processor described herein, may use the computersystem configuration and the Internet connection shown in FIG. 4 . Thesystem 140 may be used as a portable electronic device such as anotebook/laptop computer, a media player (e.g., MP3 based or videoplayer), a cellular phone, a Personal Digital Assistant (PDA), a glucosemonitor device, an insulin delivery device, an image processing device(e.g., a digital camera or video recorder), and/or any other handheldcomputing devices, or a combination of any of these devices.

Note that while FIG. 4 illustrates various components of a computersystem, it is not intended to represent any particular architecture ormanner of interconnecting the components; as such details are notgermane to the present disclosure. It will also be appreciated thatnetwork computers, handheld computers, cell phones and other dataprocessing systems which have fewer components or perhaps morecomponents may also be used. The computer system of FIG. 4 may, forexample, be an Apple Macintosh computer or Power Book, or an IBMcompatible PC. Computer system 140 includes a bus 137, an interconnect,or other communication mechanism for communicating information, and aprocessor 138, commonly in the form of an integrated circuit, coupledwith bus 137 for processing information and for executing the computerexecutable instructions. Computer system 140 also includes a main memory134, such as a Random Access Memory (RAM) or other dynamic storagedevice, coupled to bus 137 for storing information and instructions tobe executed by processor 138.

Main memory 134 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 138. Computer system 140 further includes a ReadOnly Memory (ROM) 136 (or other non-volatile memory) or other staticstorage device coupled to bus 137 for storing static information andinstructions for processor 138. A storage device 135, such as a magneticdisk or optical disk, a hard disk drive for reading from and writing toa hard disk, a magnetic disk drive for reading from and writing to amagnetic disk, and/or an optical disk drive (such as DVD) for readingfrom and writing to a removable optical disk, is coupled to bus 137 forstoring information and instructions. The hard disk drive, magnetic diskdrive, and optical disk drive may be connected to the system bus by ahard disk drive interface, a magnetic disk drive interface, and anoptical disk drive interface, respectively. The drives and theirassociated computer-readable media provide non-volatile storage ofcomputer readable instructions, data structures, program modules andother data for the general purpose computing devices. The computersystem 140 can include an Operating System (OS) stored in a non-volatilestorage for managing the computer resources and provides theapplications and programs with an access to the computer resources andinterfaces. An operating system commonly processes system data and userinput, and responds by allocating and managing tasks and internal systemresources, such as controlling and allocating memory, prioritizingsystem requests, controlling input and output devices, facilitatingnetworking and managing files. Non-limiting examples of operatingsystems are Microsoft Windows, Mac OS X, and Linux.

Computer system 140 may be coupled via bus 137 to a display 131, such asa Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), a flat screenmonitor, a touch screen monitor or similar means for displaying text andgraphical data to a user. The display may be connected via a videoadapter for supporting the display. The display allows a user to view,enter, and/or edit information that is relevant to the operation of thesystem. An input device 132, including alphanumeric and other keys, iscoupled to bus 137 for communicating information and command selectionsto processor 138. Another type of user input device is cursor control133, such as a mouse, a trackball, or cursor direction keys forcommunicating direction information and command selections to processor138 and for controlling cursor movement on display 131. This inputdevice can, for example, have two degrees of freedom in two axes, afirst axis (e.g., x) and a second axis (e.g., y), that allows the deviceto specify positions in a plane.

The computer system 140 may be used for implementing the methods andtechniques described herein. According to one embodiment, those methodsand techniques are performed by computer system 140 in response toprocessor 138 executing one or more sequences of one or moreinstructions contained in main memory 134. Such instructions may be readinto main memory 134 from another computer-readable medium, such asstorage device 135. Execution of the sequences of instructions containedin main memory 134 causes processor 138 to perform the process stepsdescribed herein. In alternative embodiments, hard-wired circuitry maybe used in place of or in combination with software instructions toimplement the arrangement. Thus, embodiments disclosed are not limitedto any specific combination of hardware circuitry and software.

The term “computer-readable medium” (or “machine-readable medium”) asused herein is an extensible term that refers to any medium or anymemory, that participates in providing instructions to a processor,(such as processor 138) for execution, or any mechanism for storing ortransmitting information in a form readable by a machine (e.g., acomputer). Such a medium may store computer-executable instructions tobe executed by a processing element and/or control logic, and data whichis manipulated by a processing element and/or control logic, and maytake many forms, including but not limited to, non-volatile medium,volatile medium, and transmission medium. Transmission media includescoaxial cables, copper wire and fiber optics, including the wires thatinclude bus 137. Transmission media can also take the form of acousticor light waves, such as those generated during radio-wave and infrareddata communications, or other form of propagated signals (e.g., carrierwaves, infrared signals, digital signals, etc.). Common forms ofcomputer-readable media include, for example, a floppy disk, a flexibledisk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM,any other optical medium, punch-cards, paper-tape, any other physicalmedium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM,any other memory chip or cartridge, a carrier wave as describedhereinafter, or any other medium from which a computer can read.

Various forms of computer-readable media may be involved in carrying oneor more sequences of one or more instructions to processor 138 forexecution. For example, the instructions may initially be carried on amagnetic disk of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 140 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 137. Bus 137 carries the data tomain memory 134, from which processor 138 retrieves and executes theinstructions. The instructions received by main memory 134 mayoptionally be stored on storage device 135 either before or afterexecution by processor 138.

Computer system 140 also includes a communication interface 141 coupledto bus 137. Communication interface 141 provides a two-way datacommunication coupling to a network link 139 that is connected to alocal network 111. For example, communication interface 141 may be anIntegrated Services Digital Network (ISDN) card or a modem to provide adata communication connection to a corresponding type of telephone line.

As another non-limiting example, communication interface 141 may be alocal area network (LAN) card to provide a data communication connectionto a compatible LAN. For example, Ethernet based connection based onIEEE802.3 standard may be used such as 10/100 BaseT, 1000 BaseT (gigabitEthernet), 10 gigabit Ethernet (10 GE or 10 GbE or 10 GigE per IEEE Std802.3 ae-2002 as standard), 40 Gigabit Ethernet (40 GbE), or 100 GigabitEthernet (100 GbE as per Ethernet standard IEEE P802.3ba), as describedin Cisco Systems, Inc. Publication number 1-587005-001-3 (June 1999),“Internetworking Technologies Handbook”, Chapter 7: “EthernetTechnologies”, pages 7-1 to 7-38, which is incorporated in its entiretyfor all purposes as if fully set forth herein. In such a case, thecommunication interface 141 can include a LAN transceiver or a modem,such as Standard Microsystems Corporation (SMSC) LAN91C111 10/100Ethernet transceiver described in the Standard Microsystems Corporation(SMSC) data-sheet “LAN91C111 10/100 Non-PCI Ethernet Single ChipMAC+PHY” Data-Sheet, Rev. 15 (Feb. 20, 2004), which is incorporated inits entirety for all purposes as if fully set forth herein.

Wireless links may also be implemented. In any such implementation,communication interface 141 sends and receives electrical,electromagnetic or optical signals that carry digital data streamsrepresenting various types of information.

Network link 139 can provide data communication through one or morenetworks to other data devices. For example, network link 139 mayprovide a connection through local network 111 to a host computer or todata equipment operated by an Internet Service Provider (ISP) 142. ISP142 in turn provides data communication services through the world widepacket data communication network Internet 11. Local network 111 andInternet 11 both use electrical, electromagnetic or optical signals thatcarry digital data streams. The signals through the various networks andthe signals on the network link 139 and through the communicationinterface 141, which carry the digital data to and from computer system140, are exemplary forms of carrier waves transporting the information.

A received code may be executed by processor 138 as it is received,and/or stored in storage device 135, or other non-volatile storage forlater execution. In this manner, computer system 140 may obtainapplication code in the form of a carrier wave.

The concept of method and system for virtualization of virtual basalrates from planned and historical insulin delivery have been developedand disclosed herein; and may be implemented and utilized with therelated processors, networks, computer systems, internet, and componentsand functions according to the schemes disclosed herein.

FIG. 6 illustrates a block diagram of an example machine 400 upon whichone or more embodiments (e.g., discussed methodologies) can beimplemented (e.g., run).

Examples of machine 400 can include logic, one or more components,circuits (e.g., modules), or mechanisms. Circuits are tangible entitiesconfigured to perform certain operations. In an example, circuits can bearranged (e.g., internally or with respect to external entities such asother circuits) in a specified manner. In an example, one or morecomputer systems (e.g., a standalone, client or server computer system)or one or more hardware processors (processors) can be configured bysoftware (e.g., instructions, an application portion, or an application)as a circuit that operates to perform certain operations as describedherein. In an example, the software can reside (1) on a non-transitorymachine readable medium or (2) in a transmission signal. In an example,the software, when executed by the underlying hardware of the circuit,causes the circuit to perform the certain operations.

In an example, a circuit can be implemented mechanically orelectronically. For example, a circuit can include dedicated circuitryor logic that is specifically configured to perform one or moretechniques such as discussed above, such as including a special-purposeprocessor, a field programmable gate array (FPGA) or anapplication-specific integrated circuit (ASIC). In an example, a circuitcan include programmable logic (e.g., circuitry, as encompassed within ageneral-purpose processor or other programmable processor) that can betemporarily configured (e.g., by software) to perform the certainoperations. It will be appreciated that the decision to implement acircuit mechanically (e.g., in dedicated and permanently configuredcircuitry), or in temporarily configured circuitry (e.g., configured bysoftware) can be driven by cost and time considerations.

Accordingly, the term “circuit” is understood to encompass a tangibleentity, be that an entity that is physically constructed, permanentlyconfigured (e.g., hardwired), or temporarily (e.g., transitorily)configured (e.g., programmed) to operate in a specified manner or toperform specified operations. In an example, given a plurality oftemporarily configured circuits, each of the circuits need not beconfigured or instantiated at any one instance in time. For example,where the circuits include a general-purpose processor configured viasoftware, the general-purpose processor can be configured as respectivedifferent circuits at different times. Software can accordinglyconfigure a processor, for example, to constitute a particular circuitat one instance of time and to constitute a different circuit at adifferent instance of time.

In an example, circuits can provide information to, and receiveinformation from, other circuits. In this example, the circuits can beregarded as being communicatively coupled to one or more other circuits.Where multiple of such circuits exist contemporaneously, communicationscan be achieved through signal transmission (e.g., over appropriatecircuits and buses) that connect the circuits. In embodiments in whichmultiple circuits are configured or instantiated at different times,communications between such circuits can be achieved, for example,through the storage and retrieval of information in memory structures towhich the multiple circuits have access. For example, one circuit canperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further circuit canthen, at a later time, access the memory device to retrieve and processthe stored output. In an example, circuits can be configured to initiateor receive communications with input or output devices and can operateon a resource (e.g., a collection of information).

The various operations of method examples described herein can beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors can constitute processor-implementedcircuits that operate to perform one or more operations or functions. Inan example, the circuits referred to herein can includeprocessor-implemented circuits.

Similarly, the methods described herein can be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod can be performed by one or processors or processor-implementedcircuits. The performance of certain of the operations can bedistributed among the one or more processors, not only residing within asingle machine, but deployed across a number of machines. In an example,the processor or processors can be located in a single location (e.g.,within a home environment, an office environment or as a server farm),while in other examples the processors can be distributed across anumber of locations.

The one or more processors can also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations can be performed by a group of computers (as examples ofmachines including processors), with these operations being accessiblevia a network (e.g., the Internet) and via one or more appropriateinterfaces (e.g., Application Program Interfaces (APIs).)

Example embodiments (e.g., apparatus, systems, or methods) can beimplemented in digital electronic circuitry, in computer hardware, infirmware, in software, or in any combination thereof. Exampleembodiments can be implemented using a computer program product (e.g., acomputer program, tangibly embodied in an information carrier or in amachine readable medium, for execution by, or to control the operationof, data processing apparatus such as a programmable processor, acomputer, or multiple computers).

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a software module,subroutine, or other unit suitable for use in a computing environment. Acomputer program can be deployed to be executed on one computer or onmultiple computers at one site or distributed across multiple sites andinterconnected by a communication network.

In an example, operations can be performed by one or more programmableprocessors executing a computer program to perform functions byoperating on input data and generating output. Examples of methodoperations can also be performed by, and example apparatus can beimplemented as, special purpose logic circuitry (e.g., a fieldprogrammable gate array (FPGA) or an application-specific integratedcircuit (ASIC)).

The computing system can include clients and servers. A client andserver are generally remote from each other and generally interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that both hardware and software architectures requireconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor), or a combinationof permanently and temporarily configured hardware can be a designchoice. Below are set out hardware (e.g., machine 400) and softwarearchitectures that can be deployed in example embodiments.

In an example, the machine 400 can operate as a standalone device or themachine 400 can be connected (e.g., networked) to other machines.

In a networked deployment, the machine 400 can operate in the capacityof either a server or a client machine in server-client networkenvironments. In an example, machine 400 can act as a peer machine inpeer-to-peer (or other distributed) network environments. The machine400 can be a personal computer (PC), a tablet PC, a set-top box (STB), aPersonal Digital Assistant (PDA), a mobile telephone, a web appliance, anetwork router, switch or bridge, or any machine capable of executinginstructions (sequential or otherwise) specifying actions to be taken(e.g., performed) by the machine 400. Further, while only a singlemachine 400 is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein.

An exemplary machine (e.g., computer system) 400 can include a processor402 (e.g., a central processing unit (CPU), a graphics processing unit(GPU) or both), a main memory 404 and a static memory 406, some or allof which can communicate with each other via a bus 408. The machine 400can further include a display unit 410, an alphanumeric input device 412(e.g., a keyboard), and a user interface (UI) navigation device 411(e.g., a mouse). In an example, the display unit 410, input device 412and UI navigation device 414 can be a touch screen display. The machine400 can additionally include a storage device (e.g., drive unit) 416, asignal generation device 418 (e.g., a speaker), a network interfacedevice 420, and one or more sensors 421, such as a global positioningsystem (GPS) sensor, compass, accelerometer, or other sensor.

The storage device 416 can include a machine readable medium 422 onwhich is stored one or more sets of data structures or instructions 424(e.g., software) embodying or utilized by any one or more of themethodologies or functions described herein. The instructions 424 canalso reside, completely or at least partially, within the main memory404, within static memory 406, or within the processor 402 duringexecution thereof by the machine 400. In an example, one or anycombination of the processor 402, the main memory 404, the static memory406, or the storage device 416 can constitute machine readable media.

While the machine readable medium 422 is illustrated as a single medium,the term “machine readable medium” can include a single medium ormultiple media (e.g., a centralized or distributed database, and/orassociated caches and servers) that configured to store the one or moreinstructions 424. The term “machine readable medium” can also be takento include any tangible medium that is capable of storing, encoding, orcarrying instructions for execution by the machine and that cause themachine to perform any one or more of the methodologies of the presentdisclosure or that is capable of storing, encoding or carrying datastructures utilized by or associated with such instructions. The term“machine readable medium” can accordingly be taken to include, but notbe limited to, solid-state memories, and optical and magnetic media.Specific examples of machine readable media can include non-volatilememory, including, by way of example, semiconductor memory devices(e.g., Electrically Programmable Read-Only Memory (EPROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM)) and flash memorydevices; magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions 424 can further be transmitted or received over acommunications network 426 using a transmission medium via the networkinterface device 420 utilizing any one of a number of transfer protocols(e.g., frame relay, IP, TCP, UDP, HTTP, etc.). Example communicationnetworks can include a local area network (LAN), a wide area network(WAN), a packet data network (e.g., the Internet), mobile telephonenetworks (e.g., cellular networks), Plain Old Telephone (POTS) networks,and wireless data networks (e.g., IEEE 802.11 standards family known asWi-Fi®, IEEE 802.16 standards family known as WiMax®), peer-to-peer(P2P) networks, among others. The term “transmission medium” shall betaken to include any intangible medium that is capable of storing,encoding or carrying instructions for execution by the machine, andincludes digital or analog communications signals or other intangiblemedium to facilitate communication of such software.

It should be appreciated that any of the components or modules referredto with regards to any of the present exemplary embodiments discussedherein, may be integrally or separately formed with one another.Further, redundant functions or structures of the components or modulesmay be implemented. Moreover, the various components may be communicatedlocally and/or remotely with any user/clinician/patient ormachine/system/computer/processor. Moreover, the various components maybe in communication via wireless and/or hardwire or other desirable andavailable communication means, systems and hardware. Moreover, variouscomponents and modules may be substituted with other modules orcomponents that provide similar functions.

It should be appreciated that the device and related componentsdiscussed herein may take on all shapes along the entire continualgeometric spectrum of manipulation of x, y and z planes to provide andmeet the anatomical, environmental, and structural demands andoperational requirements. Moreover, locations and alignments of thevarious components may vary as desired or required.

It should be appreciated that various sizes, dimensions, contours,rigidity, shapes, flexibility and materials of any of the components orportions of components in the various embodiments discussed throughoutmay be varied and utilized as desired or required.

It should be appreciated that while some dimensions are provided on theaforementioned figures, the device may constitute various sizes,dimensions, contours, rigidity, shapes, flexibility and materials as itpertains to the components or portions of components of the device, andtherefore may be varied and utilized as desired or required.

In summary, while the present disclosure has been described with respectto specific embodiments, many modifications, variations, alterations,substitutions, and equivalents will be apparent to those skilled in theart. The present disclosure is not to be limited in scope by thespecific embodiment described herein. Indeed, various modifications ofthe present disclosure, in addition to those described herein, will beapparent to those of skill in the art from the foregoing description andaccompanying drawings. Accordingly, the exemplary embodiment is to beconsidered as limited only by the spirit and scope of the disclosure,including all modifications and equivalents.

Still other embodiments will become readily apparent to those skilled inthis art from reading the above-recited detailed description anddrawings of certain exemplary embodiments. It should be understood thatnumerous variations, modifications, and additional embodiments arepossible, and accordingly, all such variations, modifications, andembodiments are to be regarded as being within the spirit and scope ofthis application. For example, regardless of the content of any portion(e.g., title, field, background, summary, abstract, drawing figure,etc.) of this application, unless clearly specified to the contrary,there is no requirement for the inclusion in any claim herein or of anyapplication claiming priority hereto of any particular described orillustrated activity or element, any particular sequence of suchactivities, or any particular interrelationship of such elements.Moreover, any activity can be repeated, any activity can be performed bymultiple entities, and/or any element can be duplicated. Further, anyactivity or element can be excluded, the sequence of activities canvary, and/or the interrelationship of elements can vary. Unless clearlyspecified to the contrary, there is no requirement for any particulardescribed or illustrated activity or element, any particular sequence orsuch activities, any particular size, speed, material, dimension orfrequency, or any particularly interrelationship of such elements.Accordingly, the descriptions and drawings are to be regarded asillustrative in nature, and not as restrictive. Moreover, when anynumber or range is described herein, unless clearly stated otherwise,that number or range is approximate. When any range is descried herein,unless clearly stated otherwise, that range includes all values thereinand all sub ranges therein. Any information in any material (e.g., aUnited States/foreign patent, United States/foreign patent application,book, article, etc.) that has been incorporated by reference herein, isonly incorporated by reference to the extent that no conflict existsbetween such information and the other statements and drawings set forthherein. In the event of such conflict, including a conflict that wouldrender invalid any claim herein or seeking priority hereto, then anysuch conflicting information in such incorporated by reference materialis specifically not incorporated by reference herein.

It will be appreciated by those skilled in the art that the presentdisclosure can be embodied in other specific forms without departingfrom the spirit or essential characteristics thereof. The presentlydisclosed embodiments are therefore considered in all respects to beillustrative and not restricted. The scope of the disclosure isindicated by the appended claims rather than the foregoing descriptionand all changes that come within the meaning and range and equivalencethereof are intended to be embraced therein.

What is claimed is:
 1. An insulin dispensing device, comprising: aprocessor configured to receive insulin dosing schedule information,psychological stress level data, and body mass index (BMI) data; asensor configured to generate a blood glucose level measurement, whereinthe sensor is calibrated as a function of the psychological stress leveldata and the BMI data, and wherein the processor is configured to:monitor and detect changes of the blood glucose level measurement thatare determined to have occurred as a function of changes of thepsychological stress level data, and identify a BMI value thatcounteracts a detected change in the blood glucose level measurement;and wherein the processor is configured to change the insulin dosingschedule information in accordance with the counteracting BMI data. 2.The insulin dispensing device of claim 1, wherein the insulin dosingschedule information is input.
 3. The insulin dispensing device of claim1 in combination with: an insulin pump, wherein the insulin dosingschedule information is received from the insulin pump.
 4. The insulindispensing device of claim 1, wherein the psychological stress leveldata is measured on a scale.
 5. The insulin dispensing device of claim4, wherein the scale is a five point Likert scale.
 6. The insulindispensing device of claim 1, wherein the processor is configured tocorrelate a detected increase in a value of the psychological stresslevel data with an increase in a value of the blood glucose levelmeasurement.
 7. The insulin dispensing device of claim 1, wherein theprocessor is configured to correlate a detected decrease in a value ofthe psychological stress level data with a decrease in a value of theblood glucose level measurement.
 8. The insulin dispensing device ofclaim 1, wherein the processor is configured to correlate acounteraction of a BMI data increase with an increase in a value of theBMI data.
 9. The insulin dispensing device of claim 8, wherein theprocessor is configured to detect a decrease in a value of a bloodglucose level measurement data when the increase in the value of the BMIdata crosses a threshold.
 10. The insulin dispensing device of claim 1,wherein the processor is configured to correlate the counteraction by aBMI data with a higher uptake of glucose.
 11. The insulin dispensingdevice of claim 1 in combination with: a display, wherein the processoris configured to categorize and display the BMI data as normal BMI,overweight BMI, or obese BMI.
 12. A method for controlling insulindispensing based on insulin sensitivity, the method comprising:receiving insulin dosing schedule information, psychological stresslevel data, and body mass index (BMI) data; generating a blood glucoselevel measurement as a function of the psychological stress level dataand the BMI data; monitoring and detecting changes of the blood glucoselevel measurement that are determined to have occurred as a function ofchanges of the psychological stress level data; identifying a BMI valuethat counteracts a detected change in the blood glucose levelmeasurement; updating the insulin dosing schedule information inaccordance with the counteracting BMI data; and controlling insulindispensing to provide insulin dosing based on the updated insulin dosingschedule information.
 13. The method of claim 12 wherein: a processorreceives the insulin dosing schedule information, psychological stresslevel data, and body mass index (BMI) data.
 14. The method of claim 12wherein: a sensor generates the blood glucose level measurement.
 15. Themethod of claim 12 wherein: a processor monitors and detects changes ofthe blood glucose level measurement.
 16. The method of claim 12 wherein:a processor identifying the BMI value that counteracts the detectedchange in the blood glucose level measurement.
 17. The method of claim12 wherein: an insulin dispensing device performs the insulindispensing.
 18. A non-transitory computer readable recording mediumencoded with a computer program including instructions causing controlof insulin dispensing for an insulin dispensing device based on insulinsensitivity, the program causing the insulin dispensing device to:receive insulin dosing schedule information, psychological stress leveldata, and body mass index (BMI) data; generate a blood glucose levelmeasurement as a function of the psychological stress level data and theBMI data; monitor and detect changes of the blood glucose levelmeasurement that are determined to have occurred as a function ofchanges of the psychological stress level data; identify a BMI valuethat counteracts a detected change in the blood glucose levelmeasurement; update the insulin dosing schedule information inaccordance with the counteracting BMI data; and control insulindispensing to provide insulin dosing based on the updated insulin dosingschedule information.
 19. A non-transitory computer readable recordingmedium encoded with a computer program including instructions causingcontrol of glucose monitoring for a glucose monitoring device, theprogram causing the glucose monitoring device to: receive insulin dosingschedule information, psychological stress level data, and body massindex (BMI) data; generate a blood glucose level measurement as afunction of the psychological stress level data and the BMI data; andmonitor and detect changes of the blood glucose level measurement thatare determined to have occurred as a function of changes of thepsychological stress level data.
 20. The non-transitory computerreadable recording medium of claim 19, wherein the program causes theglucose monitoring device to: identify a BMI value that counteracts adetected change in the blood glucose level measurement.